Self-Driving Car Engineer Nanodegree¶
Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Import Packages¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import os
%matplotlib inline
1 - Caliberation¶
In [2]:
nx = 9
ny = 6
camera_cal_img_dir = "./camera_cal/"
camera_cal_output_dir = "./camera_cal_output/"
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
camera_cal_images = glob.glob(camera_cal_img_dir + '*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(camera_cal_images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
image_name = os.path.split(fname)[1]
write_name = camera_cal_output_dir + 'cornersfound_' + image_name
cv2.imwrite(write_name,img)
#cv2.imshow('img', img)
cv2.waitKey(500)
cv2.destroyAllWindows()
test on images¶
In [3]:
# Test undistortion on an image
img = cv2.imread(camera_cal_img_dir + 'calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
Out[3]:
In [4]:
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "wide_dist_pickle.p", "wb" ) )
In [5]:
undistorted_dir = "undistorted_images/"
# load pickled distortion matrix
with open('wide_dist_pickle.p', mode='rb') as f:
dist_pickle = pickle.load(f)
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]
# Visualize undistortion
# Step through the list and search for chessboard corners
for idx, fname in enumerate(camera_cal_images):
img = cv2.imread(fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
image_name = os.path.split(fname)[1]
os.makedirs(camera_cal_output_dir + undistorted_dir, exist_ok=True)
write_name = camera_cal_output_dir + undistorted_dir + 'undistorted_' + image_name
cv2.imwrite(write_name,dst)
print(write_name)
#cv2.imshow('dst', dst)
cv2.waitKey(500)
cv2.destroyAllWindows()
2 - Apply a distortion correction to raw images.¶
In [6]:
test_images_dir = "./test_images/"
output_images_dir = "./output_images/"
undistorted_dir = 'undistorted_images/'
test_images = glob.glob(test_images_dir + '*.jpg')
for idx, fname in enumerate(test_images):
img = cv2.imread(fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
image_name=os.path.split(fname)[1]
os.makedirs(output_images_dir + undistorted_dir, exist_ok=True)
cv2.imwrite(output_images_dir + undistorted_dir + image_name ,dst)
print(image_name)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image' + str(idx+1), fontsize=30)
ax2.imshow(cv2.cvtColor(dst, cv2.COLOR_BGR2RGB))
ax2.set_title('Undistorted Image' + str(idx+1), fontsize=30)
cv2.destroyAllWindows()
3 - Gradients and color transforms¶
I used 5 kinds of gradient thresholds:
- Along the X and Y axis.
- Directional gradient with thresholds of 0.7 and 1.3 degrees.
- Magnitude of the Gradient with thresholds of 30 and 100.
- S channel threshold since it picks up the lines well.
- L channel threshold.
In [7]:
def abs_sobel_thresh(gray, orient='x', sobel_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take the derivative in x or y given orient = 'x' or 'y'
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
else:
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
# Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a mask of 1's where the scaled gradient magnitude
grad_binary = np.zeros_like(scaled_sobel)
grad_binary[(scaled_sobel >= sobel_thresh[0]) & (scaled_sobel <= sobel_thresh[1])] = 1
# Return this mask as your binary_output image
return grad_binary
def mag_thresh(gray, sobel_kernel=3, mag_thresh=(0, 255)):
# Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Scale to 8-bit (0 - 255) and convert to type = np.uint8
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary mask where mag thresholds are met
mag_binary = np.zeros_like(gradmag)
mag_binary[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return this mask as your binary_output image
return mag_binary
def dir_threshold(gray, sobel_kernel=3, thresh=(0, np.pi/2)):
# Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the x and y gradients
# Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
# Create a binary mask where direction thresholds are met
# Return this mask as your binary_output image
dir_binary = np.zeros_like(absgraddir)
dir_binary[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return dir_binary
def get_thresholded_image(img):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
height, width = gray.shape
ksize = 15 # Choose a larger odd number to smooth gradient measurements
gradx = abs_sobel_thresh(gray, orient='x', sobel_thresh=(10, 200))
grady = abs_sobel_thresh(gray, orient='y', sobel_thresh=(10, 200))
mag_binary = mag_thresh(gray, sobel_kernel=ksize, mag_thresh=(30, 100))
dir_binary = dir_threshold(gray, sobel_kernel=ksize, thresh=(0.7, 1.3))
# combine the gradient , magnitude thresolds and direction thresholds.
combined_condition = ((gradx == 1) & (grady == 1) | (mag_binary == 1) & (dir_binary == 1))
# color channel thresholds
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
l_channel = hls[:,:,1]
# Threshold color channel
s_thresh = (170, 255)
s_condition = (s_channel > s_thresh[0]) & (s_channel <= s_thresh[1])
# We put a threshold on the L channel to avoid pixels which have shadows and as a result darker.
l_thresh = (120, 255)
l_condition = (l_channel > l_thresh[0]) & (l_channel <= l_thresh[1])
color_combined = np.zeros_like(s_channel)
color_combined[(l_condition & (s_condition | combined_condition))] = 1
return color_combined
Test on Images¶
In [8]:
thresholded_dir = 'thresholded_images/'
undistorted_images = glob.glob(output_images_dir + undistorted_dir + '*.jpg')
for idx, fname in enumerate(undistorted_images):
img = cv2.imread(fname)
thresholded = get_thresholded_image(img)
image_name=os.path.split(fname)[1]
os.makedirs(output_images_dir + thresholded_dir, exist_ok=True)
cv2.imwrite(output_images_dir + thresholded_dir + image_name ,thresholded)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image' + str(idx+1), fontsize=30)
ax2.imshow(thresholded, cmap='gray')
ax2.set_title('Thresholded Image' + str(idx+1), fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
cv2.destroyAllWindows()
4 - Perspective transform to rectify binary image ("birds-eye view").¶
In [9]:
# Define perspective transform function
def get_warped_image(img, trans=True):
# Define calibration box in source (original) and destination (desired and warped) coordinates
img_size = (img.shape[1], img.shape[0])
# Four source coordinates
bottom_left = [220,720]
bottom_right = [1110, 720]
top_left = [570, 470]
top_right = [722, 470]
src = np.float32([bottom_left,bottom_right,top_right,top_left])
pts = np.array([bottom_left,bottom_right,top_right,top_left], np.int32)
pts = pts.reshape((-1,1,2))
copy = img.copy()
cv2.polylines(copy,[pts],True,(255,0,0), thickness=3)
# Four desired coordinates
bottom_left = [320,720]
bottom_right = [920, 720]
top_left = [320, 1]
top_right = [920, 1]
dst = np.float32([bottom_left,bottom_right,top_right,top_left])
if trans:
# compute the perspective transform M
M = cv2.getPerspectiveTransform(src, dst)
else:
# Could compute the inverse also by swaping the input parameters
M = cv2.getPerspectiveTransform(dst, src)
# Create warped image - uses linear interpotation
warped = cv2.warpPerspective(img, M, img_size , flags=cv2.INTER_LINEAR)
return warped
Test on Images¶
In [10]:
original_images = glob.glob(test_images_dir + '*.jpg')
warped_dir = 'warped_images/'
for idx, fname in enumerate(original_images):
img = cv2.imread(fname)
dst = cv2.undistort(img, mtx, dist, None, mtx)
#thresholded = get_thresholded_image(dst)
warped = get_warped_image(dst)
image_name=os.path.split(fname)[1]
os.makedirs(output_images_dir + warped_dir, exist_ok=True)
cv2.imwrite(output_images_dir + warped_dir + image_name ,warped)
# Four source coordinates
bottom_left = [220,720]
bottom_right = [1110, 720]
top_left = [570, 470]
top_right = [722, 470]
src = np.float32([bottom_left,bottom_right,top_right,top_left])
pts = np.array([bottom_left,bottom_right,top_right,top_left], np.int32)
pts = pts.reshape((-1,1,2))
copy = img.copy()
cv2.polylines(copy,[pts],True,(0,0,255), thickness=3)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(cv2.cvtColor(copy, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image' + str(idx+1), fontsize=50)
ax2.imshow(cv2.cvtColor(warped, cv2.COLOR_BGR2RGB))
ax2.set_title('Warped Image' + str(idx+1), fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
cv2.destroyAllWindows()
In [11]:
def hist(img):
# Grab only the bottom half of the image
# Lane lines are likely to be mostly vertical nearest to the car
bottom_half = img[img.shape[0]//2:,:]
# Sum across image pixels vertically - make sure to set an `axis`
# i.e. the highest areas of vertical lines should be larger values
histogram = np.sum(bottom_half, axis=0)
return histogram
In [12]:
img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(img, mtx, dist, None, mtx)
thresholded = get_thresholded_image(dst)
warped = get_warped_image(thresholded)
histogram = hist(warped)
plt.plot(histogram)
Out[12]:
5-2 Finding the Lines: Sliding Window¶
In [13]:
def find_lane_pixels(binary_warped):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# HYPERPARAMETERS
# Choose the number of sliding windows
nwindows = 9
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Set height of windows - based on nwindows above and image shape
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated later for each window in nwindows
leftx_current = leftx_base
rightx_current = rightx_base
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),
(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),
(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window #
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices (previously was a list of lists of pixels)
try:
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
except ValueError:
# Avoids an error if the above is not implemented fully
pass
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return leftx, lefty, rightx, righty, out_img
def fit_polynomial(binary_warped):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
## Visualization ##
# Colors in the left and right lane regions
out_img[lefty, leftx] = [255, 0, 0]
out_img[righty, rightx] = [0, 0, 255]
# Plots the left and right polynomials on the lane lines
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
return out_img
In [14]:
img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(img, mtx, dist, None, mtx)
thresholded = get_thresholded_image(dst)
warped = get_warped_image(thresholded)
out_img = fit_polynomial(warped)
plt.imshow(out_img)
Out[14]:
5-3 Finding the Lines: Search from Prior¶
In [15]:
def fit_poly(img_shape, leftx, lefty, rightx, righty):
if leftx.size == 0:
left_fitx = None
right_fitx = None
ploty = None
else:
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
return left_fitx, right_fitx, ploty
def search_around_poly(binary_warped):
# HYPERPARAMETER
# Choose the width of the margin around the previous polynomial to search
# The quiz grader expects 100 here, but feel free to tune on your own!
margin = 100
# Polynomial fit values from the previous frame
# Make sure to grab the actual values from the previous step in your project!
left_fit = np.array([ 2.13935315e-04, -3.77507980e-01, 4.76902175e+02])
right_fit = np.array([4.17622148e-04, -4.93848953e-01, 1.11806170e+03])
# Grab activated pixels
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
### TO-DO: Set the area of search based on activated x-values ###
### within the +/- margin of our polynomial function ###
### Hint: consider the window areas for the similarly named variables ###
### in the previous quiz, but change the windows to our new search area ###
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
if leftx.size == 0 or lefty.size == 0 or rightx.size == 0 or righty.size == 0 :
left_fitx,right_fitx,ploty = search_lane(warped)
else:
# Fit new polynomials
left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
## Visualization ##
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
return result, left_fitx, right_fitx, ploty
In [16]:
img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(img, mtx, dist, None, mtx)
thresholded = get_thresholded_image(dst)
warped = get_warped_image(thresholded)
result, left_fitx, right_fitx, ploty = search_around_poly(warped)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, warped.shape[1])
plt.ylim(warped.shape[0],0)
Out[16]:
6 - Determine the curvature of the lane and vehicle position with respect to center.¶
In [17]:
def measure_radius_of_curvature(x_values, warped):
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# If no pixels were found return None
y_points = np.linspace(0, warped.shape[0]-1, warped.shape[0])
y_eval = np.max(y_points)
# Fit new polynomials to x,y in world space
fit_cr = np.polyfit(y_points*ym_per_pix, x_values*xm_per_pix, 2)
curverad = ((1 + (2*fit_cr[0]*y_eval*ym_per_pix + fit_cr[1])**2)**1.5) / np.absolute(2*fit_cr[0])
return curverad
In [18]:
img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(img, mtx, dist, None, mtx)
thresholded = get_thresholded_image(dst)
warped = get_warped_image(thresholded)
result, left_fitx, right_fitx, ploty = search_around_poly(warped)
left_curve_rad = measure_radius_of_curvature(left_fitx, warped)
right_curve_rad = measure_radius_of_curvature(right_fitx, warped)
average_curve_rad = (left_curve_rad + right_curve_rad)/2
curvature_string = "Radius of curvature: %.2f m" % average_curve_rad
print(curvature_string)
# compute the offset from the center
lane_center = (right_fitx[719] + left_fitx[719])/2
xm_per_pix = 3.7/700 # meters per pixel in x dimension
center_offset_pixels = abs(img_size[0]/2 - lane_center)
center_offset_mtrs = xm_per_pix*center_offset_pixels
offset_string = "Center offset: %.2f m" % center_offset_mtrs
print(offset_string)
7 - Warp the detected lane boundaries back onto the original image.¶
In [19]:
def draw_poly(img, warped, left_fitx, right_fitx):
out_img = np.dstack((warped, warped, warped))*255
ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0])
left_line_window = np.array(np.transpose(np.vstack([left_fitx, ploty])))
right_line_window = np.array(np.flipud(np.transpose(np.vstack([right_fitx, ploty]))))
line_points = np.vstack((left_line_window, right_line_window))
cv2.fillPoly(out_img, np.int_([line_points]), [0,255, 0])
unwarped= get_warped_image(out_img, trans=False)
result = cv2.addWeighted(img, 1, unwarped, 0.3, 0)
return result
In [20]:
img = mpimg.imread('test_images/test6.jpg')
dst = cv2.undistort(img, mtx, dist, None, mtx)
thresholded = get_thresholded_image(dst)
warped = get_warped_image(thresholded)
poly_img, left_fitx, right_fitx, ploty = search_around_poly(warped)
result = draw_poly(img, warped, left_fitx, right_fitx)
plt.imshow(result)
Out[20]:
8 - Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.¶
In [21]:
def search_lane(binary_warped):
# Find our lane pixels first
leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)
# Fit a second order polynomial to each using `np.polyfit`
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
try:
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
except TypeError:
# Avoids an error if `left` and `right_fit` are still none or incorrect
print('The function failed to fit a line!')
left_fitx = 1*ploty**2 + 1*ploty
right_fitx = 1*ploty**2 + 1*ploty
return left_fitx, right_fitx, ploty
In [22]:
def pipeline(img):
dst = cv2.undistort(img, mtx, dist, None, mtx)
# get thresholded image
thresholded = get_thresholded_image(dst)
# perform a perspective transform
warped = get_warped_image(thresholded)
_,left_fitx,right_fitx,_ = search_around_poly(warped)
result = draw_poly(img, warped, left_fitx, right_fitx)
# compute the radius of curvature
left_curve_rad = measure_radius_of_curvature(left_fitx, warped)
right_curve_rad = measure_radius_of_curvature(right_fitx, warped)
average_curve_rad = (left_curve_rad + right_curve_rad)/2
curvature_string = "Radius of curvature: %.2f m" % average_curve_rad
# compute the offset from the center
lane_center = (right_fitx[img.shape[0]-1] + left_fitx[img.shape[0]-1])/2
xm_per_pix = 3.7/700 # meters per pixel in x dimension
center_offset_pixels = abs(img.shape[1]/2 - lane_center)
center_offset_mtrs = xm_per_pix*center_offset_pixels
offset_string = "Center offset: %.2f m" % center_offset_mtrs
cv2.putText(result,curvature_string , (100, 90), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255,255,255), thickness=2)
cv2.putText(result, offset_string, (100, 150), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255,255,255), thickness=2)
return result
Test on Images¶
In [23]:
output_images_dir = "./output_images/"
processed_dir = 'processed_images/'
original_images = glob.glob(test_images_dir + '*.jpg')
for idx, fname in enumerate(original_images):
img = cv2.imread(fname)
# Apply pipeline
processed = pipeline(img)
image_name=os.path.split(fname)[1]
os.makedirs(output_images_dir + processed_dir, exist_ok=True)
cv2.imwrite(output_images_dir + processed_dir + image_name ,processed)
# Plot the 2 images
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image'+ str(idx+1), fontsize=50)
ax2.imshow(cv2.cvtColor(processed, cv2.COLOR_BGR2RGB))
ax2.set_title('Processed Image'+ str(idx+1), fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
cv2.destroyAllWindows()
Test on Videos¶
In [24]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [25]:
output = 'project_video_output.mp4'
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(pipeline) #NOTE: this function expects color images!!
%time white_clip.write_videofile(output, audio=False)
In [26]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output))
Out[26]:
In [27]:
challenge_output = 'challenge_video_output.mp4'
clip2 = VideoFileClip("challenge_video.mp4")
challenge_clip = clip2.fl_image(pipeline) #NOTE: this function expects color images!!
%time challenge_clip.write_videofile(challenge_output, audio=False)
In [28]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(challenge_output))
Out[28]:
In [29]:
harder_challenge_output = 'harder_challenge_video_output.mp4'
clip3 = VideoFileClip("harder_challenge_video.mp4")
harder_challenge_clip = clip3.fl_image(pipeline) #NOTE: this function expects color images!!
%time harder_challenge_clip.write_videofile(harder_challenge_output, audio=False)
In [30]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(harder_challenge_output))
Out[30]: